Cancer is the second leading cause of death in the Western World, but is rapidly rising worldwide and is expected to become the number one killer in a few years. Thus, there is tremendous need to improve our understanding and ability to treat this deadly disease. Nearly all cancer types form solid tumors, abnormal tissue masses that are highly complex and dynamic. Recent evidence has pointed to a model in which tumors can be viewed as an ecosystem consisting of a diverse array of cell types that work in concert to maintain homeostasis and drive further development. This intra-tumor cellular heterogeneity has been identified as a key factor underlying progression, metastasis, and the development of drug resistance. Cell types can include neoplastic subpopulations with distinct genotypes and phenotypes that are generated through clonal evolution, differentiation from rare stem-like precursors/cancer stem cells, or most likely a combination of the two mechanisms. Host cells of diverse origins, including non-tumor epithelium, stroma, and immune subtypes, can also assist the tumor in different capacities. Thus, analyzing tumor heterogeneity and identifying the presence of key cell types have become major focus areas in tumor biology and clinical diagnostics. Knowledge of different cell types can also drive patient-specific protocols for cancer treatment.
A major challenge for solid tumor analysis is the fact that specimens are three-dimensional tissue structures. This is particularly true to assessing cellular heterogeneity and identifying rare cell types such as cancer stem cells. Tissue-based analysis methods such as histology, immunohistochemistry, and fluorescence in-situ hybridization are clinical standards that provide morphological and sub-cellular detail, but are low throughput and detection signals are difficult to quantitate and multiplex. Techniques that involve sample destruction such as genome/transcriptome sequencing, microarrays, mass spectrometry, and Western blotting can provide large amounts of molecular information but retain no context with respect to the cellular components in the original sample. Due to these limitations, researchers and clinicians are increasingly employing cell-based analysis platforms such as flow cytometry because they offer high-throughput and multiplexed information about each cell within the sample. Cell sorting can also be used to isolate rare cell types such as cancer stem cells, metastatic precursors, and drug resistance clones for additional study. The disadvantage is that the tissue must first be broken down into single cells, which requires considerable expenditure of time and effort. Moreover, dissociation can potentially damage or otherwise bias samples. Thus, tissue dissociation remains a major barrier to the application of single cell techniques to solid tumor specimens.
Tumor tissue is currently dissociated into single cells using proteolytic enzymes that digest cellular adhesion molecules and/or the underlying extracellular matrix. The tumor tissue specimen is first minced with a scalpel into ˜1-2 mm pieces. The enzyme or enzyme cocktail of choice is then applied. Trypsin is a broadly reactive protease that is highly efficient, requiring only short incubation times on the order of 15 minutes. Unfortunately, trypsin can also cleave cell surface proteins that may provide important diagnostic information or regulate cell function. For example, it has been shown that CD44, a commonly used cancer stem cell marker, is cleaved by trypsin resulting in significantly reduced expression. Collagenase is a milder alternative that digests collagen within the underlying extracellular matrix, leaving cells largely undisturbed. For this reason, collagenase has been employed for identifying and isolating cancer stem cells via CD44 or other biomarkers. However, collagenase requires long incubation times on the order of 1 to 2 hours that could negatively affect cell viability or molecular expression. Non-enzymatic options such as the calcium chelator ethylenediaminetetraacetic acid (EDTA) can also be employed, but EDTA is much less efficient and therefore used only to augment protease digestion. Following initial enzymatic or chemical treatment procedure, samples are subjected to fluid shear forces to mechanically liberate individual cells. This is typically achieved by vortexing and/or repeatedly pipetting the sample. These methods generate poorly defined shear flow environments that do not allow control over sample exposure, potentially resulting in variations across different batches or laboratories. The gentleMACS™ Dissociator (Miltenyl Biotec) is a commercial system that has been developed to standardize mechanical dissociation, but its use with tumor specimens is not common and performance is not well documented.
A final step that is used in many dissociation processes is to remove large aggregates that remain by filtering, which results in loss of sample. Taken together, tumor tissue dissociation involves multiple manual processing steps that are time-consuming and labor intensive, and there are numerous areas for which the resulting cell suspension can be improved. Notably, enzymatic digestion is either harsh or very long, large aggregates are lost to filtering, and there is no way to control whether the recovered sample contains single cells versus small clusters. Thus, new technology and methodology development is critically needed to meet all of the following goals: (1) improve dissociation efficiency so that the entire sample is recovered as single cells, (2) maximize overall cell quality in terms of viability and molecular biomarker expression, (3) decrease processing time from hours to minutes, and (4) automate the entire workflow to enable point-of-care operation and direct connection to additional downstream tasks.